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Lyu, Ilwoo
3D Shape Analysis Lab.
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dc.citation.title NEUROIMAGE -
dc.citation.volume 229 -
dc.contributor.author Lyu, Ilwoo -
dc.contributor.author Bao, S. -
dc.contributor.author Hao, L. -
dc.contributor.author Yao, J. -
dc.contributor.author Miller, J.A. -
dc.contributor.author Voorhies, W. -
dc.contributor.author Taylor, W.D. -
dc.contributor.author Bunge, S.A. -
dc.contributor.author Weiner, K.S. -
dc.contributor.author Landman, B.A. -
dc.date.accessioned 2023-12-21T16:08:02Z -
dc.date.available 2023-12-21T16:08:02Z -
dc.date.created 2021-03-09 -
dc.date.issued 2021-04 -
dc.description.abstract The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow (tertiary) sulcal regions are largely overlooked in the literature due to the scarcity of manual/well-defined annotations and their large neuroanatomical variability. In this paper, we present an automated framework for regional labeling of both primary/secondary and tertiary sulci of the dorsal portion of lateral prefrontal cortex (LPFC) using spherical convolutional neural networks. We propose two core components that enhance the inference of sulcal labels to overcome such large neuroanatomical variability: (1) surface data augmentation and (2) context-aware training. (1) To take into account neuroanatomical variability, we synthesize training data from the proposed feature space that embeds intermediate deformation trajectories of spherical data in a rigid to non-rigid fashion, which bridges an augmentation gap in conventional rotation data augmentation. (2) Moreover, we design a two-stage training process to improve labeling accuracy of tertiary sulci by informing the biological associations in neuroanatomy: inference of primary/secondary sulci and then their spatial likelihood to guide the definition of tertiary sulci. In the experiments, we evaluate our method on 13 deep and shallow sulci of human LPFC in two independent data sets with different age ranges: pediatric (N=60) and adult (N=36) cohorts. We compare the proposed method with a conventional multi-atlas approach and spherical convolutional neural networks without/with rotation data augmentation. In both cohorts, the proposed data augmentation improves labeling accuracy of deep and shallow sulci over the baselines, and the proposed context-aware training offers further improvement in the labeling of shallow sulci over the proposed data augmentation. We share our tools with the field and discuss applications of our results for understanding neuroanatomical-functional organization of LPFC and the rest of cortex (https://github.com/ilwoolyu/SphericalLabeling). © 2021 The Author(s) -
dc.identifier.bibliographicCitation NEUROIMAGE, v.229 -
dc.identifier.doi 10.1016/j.neuroimage.2021.117758 -
dc.identifier.issn 1053-8119 -
dc.identifier.scopusid 2-s2.0-85100029593 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/50087 -
dc.identifier.wosid 000629509400037 -
dc.language 영어 -
dc.publisher Academic Press Inc. -
dc.title Labeling lateral prefrontal sulci using spherical data augmentation and context-aware training -
dc.type Article -
dc.description.isOpenAccess TRUE -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Context encoder -
dc.subject.keywordAuthor Cortical surface -
dc.subject.keywordAuthor Frontal cortex -
dc.subject.keywordAuthor Spherical data augmentation -
dc.subject.keywordAuthor Sulcal labeling -
dc.subject.keywordPlus adult -
dc.subject.keywordPlus article -
dc.subject.keywordPlus cohort analysis -
dc.subject.keywordPlus convolutional neural network -
dc.subject.keywordPlus female -
dc.subject.keywordPlus human -
dc.subject.keywordPlus lateral prefrontal cortex -
dc.subject.keywordPlus major clinical study -
dc.subject.keywordPlus male -
dc.subject.keywordPlus neuroanatomy -
dc.subject.keywordPlus rotation -

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